UW iSchool Course INFO 474: focused on designing and building visualizations to better understand and communicate about pressing issues.
Published
Edited
Nov 15, 2020
Importers
chart = {
const svg = d3.create("svg").attr("viewBox", [0, 0, width, height]);
svg.append("g").call(xAxis);
svg.append("g").call(yAxis);
svg
.append("path")
.datum(data)
.attr("fill", "none")
.attr("stroke", "steelblue")
.attr("stroke-width", 1.5)
.attr("stroke-linejoin", "round")
.attr("stroke-linecap", "round")
.attr("d", line);
svg
.append("path")
.datum(data)
.attr("fill", "none")
.attr("stroke", "red")
.attr("stroke-width", 1.5)
.attr("stroke-linejoin", "round")
.attr("stroke-linecap", "round")
.attr("d", line2);
return svg.node();
}
FileAttachment("flights_time.csv")
data = d3
.csvParse(await FileAttachment("flights_time.csv").text(), d3.autoType)
.map(d => ({
value1: +d.int_pass,
date: new Date(d.Period),
// 1/10 adjustment
value: +d.dom_pass * 0.1
}))
// chart_data = data.map(d => ({
// value: +d.int_pass,
// date: new Date(d.Period)
// // value1: +d.dom_pass
// }))
line = d3
.line()
.defined(d => !isNaN(d.value))
.x(d => x(d.date))
.y(d => y(d.value))
line2 = d3
.line()
.defined(d => !isNaN(d.value1))
.x(d => x(d.date))
.y(d => y(d.value1))
x = d3
.scaleUtc()
.domain(d3.extent(data, d => d.date))
.range([margin.left, width - margin.right])
y = d3
.scaleLinear()
.domain([0, d3.max(data, d => d.value)])
.nice()
.range([height - margin.bottom, margin.top])
xAxis = g =>
g.attr("transform", `translate(0,${height - margin.bottom})`).call(
d3
.axisBottom(x)
.ticks(width / 80)
.tickSizeOuter(0)
)
yAxis = g =>
g
.attr("transform", `translate(${margin.left},0)`)
.call(d3.axisLeft(y))
.call(g => g.select(".domain").remove())
.call(g =>
g
.select(".tick:last-of-type text")
.clone()
.attr("x", 3)
.attr("text-anchor", "start")
.attr("font-weight", "bold")
.text(data.y)
)
margin = ({ top: 20, right: 30, bottom: 30, left: 50 })
height = 500
d3 = require("d3@6")
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
